A common approach to document ranking is to learn a ranking model \(f(Q,D; \theta )\) , parameterized by \(\theta \)  and trained using relevance labels, to score each retrieved candidate document \(D\)  with respect to input query \(Q\) . The ranking model can be a feature-based statistical classifier such as SVM [1]} and Boosted Tree [2]}, or a neural network model [3]}. Recently, ranking models based on pre-trained language models (PLMs) have achieved SOTA on many IR benchmarks, and have become the new standard solution to document ranking [4]}. In what follows, we present a BERT-based document ranker as an example.
